import logging
import os
import tempfile
from datetime import datetime
from random import randint

import pandas as pd
from airflow import AirflowException
from airflow.models import Variable
from task_01.target_db.repositories import TargetDBRepo

log = logging.getLogger(__name__)


class UserActionTransfer:

    def __init__(
        self,
        target_db: TargetDBRepo,
    ):
        self.target_db = target_db
        self.log = logging.getLogger(__name__)

    def generate_sample_data(self):
        """Генерация тестовых данных и сохранение в CSV файл"""
        actions = self.get_fake_user_action_data()
        csv_file_path = self.get_csv_file_path()
        actions.to_csv(csv_file_path, index=False)

        # Сохраняем путь к файлу в переменную DAG
        Variable.set("user_action_data_path", csv_file_path)

        logging.info("Sample data generated and saved to: %s", csv_file_path)
        logging.info(f"Data preview: %s", actions.head())

    def load_data_to_neo4j(self) -> None:
        csv_file_path = Variable.get("user_action_data_path")

        if not csv_file_path or not os.path.exists(csv_file_path):
            raise AirflowException("CSV file not found: %s", csv_file_path)

        # Чтение CSV файла
        user_actions = pd.read_csv(csv_file_path)
        logging.info("Loaded CSV data with %s rows", len(user_actions))

        self.target_db.save_users(user_actions)
        total_rows = self.target_db.get_number_of_users()
        logging.info("Total rows: %s", total_rows)

        # Очистка временного файла
        os.remove(csv_file_path)
        logging.info("Temporary CSV file cleaned up")

    def check_neo4j_connection(self):
        """Проверка соединения с БД"""
        try:
            result = self.target_db.check_connection()
            log.info(f"Neo4j message: {result}")
            log.info("Neo4j connection is healthy")
        except Exception as e:
            log.error(f"Neo4j connection failed: {e}")
            raise
        return result

    @staticmethod
    def get_csv_file_path() -> str:
        temp_dir = tempfile.gettempdir()
        return os.path.join(temp_dir, "user_action_data.csv")

    @staticmethod
    def get_fake_user_action_data() -> pd.DataFrame:
        actions = ["login", "purchase", "view", "logout", "search"]
        ids = list(range(1, 10001))
        action = []
        timestamp = []
        for _ in ids:
            action.append(actions[randint(0, len(actions) - 1)])
            timestamp.append(datetime.now())
        sample_data = {
            "user_id": ids,
            "action": action,
            "timestamp": timestamp
        }
        return pd.DataFrame(sample_data)